The Proportion of Energy Consumption Structure Prediction Based on Markov Chain

The Proportion of Energy Consumption Structure Prediction Based on Markov Chain

Xiaohang Ren Qian Liu Yumeng Zhang

University of Petroleum, Beijing, China.

Corresponding Author Email: 
632824435@163.com
Page: 
1-4
|
DOI: 
http://dx.doi.org/10.18280/mmep.020101
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

This paper applies a Markov Chain approach based on quadratic programming model to forecast the trends of energy production and consumption structures. The proposed models are used to simulate China’s energy consumption structure during 2003–2013 and forecast its trends from 2014 to 2020. The proposed models can effectively simulate and forecast the structures of energy production and consumption. Our study demonstrates that the growth rate of energy consumption in China will decrease, and the proportions of natural gas and other renewable energy will keep growing. However, the increasing rate is far from satisfactory; China may fail to achieve the 13th Five-Year Development Plan. Therefore the Chinese government should take more effort to achieve its energy plan.

Keywords: 

Energy, Energy structure, Markov Chain, Energy prediction, Energy policy.

1. Introduction
2. Data
3. Model
4. Conclusions
  References

1. Tso G. K. F., Yau K. K. W., Predicting Electricity Energy Consumption: A Comparison of Regression Analysis, Decision Tree and Neural Networks [J], Energy, 32(9): 1761-1768, 2007.

2. Kumar U., Jain V. K., Time Series Models (Grey-Markov, Grey Model with Rolling Mechanism and Singular Spectrum Analysis) to Forecast Energy Consumption in India [J], Energy, 35(4): 1709-1716, 2010. DOI: 10.1016/j.energy.2009.12.021.

3. Rumbayan M., Abudureyimu A., Nagasaka K., Mapping of Solar Energy Potential in Indonesia Using Artificial Neural Network and Geographical Information System

[J], Renewable and Sustainable Energy Reviews, 16(3): 1437-1449, 2012.

4. Matallanas E., Castillo-Cagigal M., Gutiérrez A, et al., Neural Network Controller for Active Demand-Side Management with PV Energy in the Residential Sector [J], Applied Energy, 91(1): 90-97, 2012. DOI: 10.1016/j.apenergy.2011.09.004.

5. Crompton P., Wu Y., Energy consumption in China: Past Trends and Future Directions [J], Energy Economics, 27(1): 195-208, 2005. DOI: 10.1016/j.eneco.2004.10.006.

6. Zhu Y. B., Wang Z., Pang L., et al., Simulation on China’s Economy and Prediction on Energy Consumption and Carbon Emission under Optimal Growth Path [J], Acta Geographica Sinica, 64(8): 935-944, 2009.

7. Krogh A., Larsson B. è., Von Heijne G., et al., Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes [J], Journal of Molecular Biology, 305(3): 567-580, 2001. DOI: 10.1006/jmbi.2000.4315.

8. Logofet D. O., Lesnaya E. V., The Mathematics of Markov Models: What Markov Chains Can Really Predict in Forest Successions [J], Ecological Modelling, 126(2): 285-298, 2000. DOI: 10.1016/S0304-3800(00)00269-6.

9. Dueker M., Neely C. J., Can Markov Switching Models Predict Excess Foreign Exchange Returns? [J], Journal of Banking & Finance, 31(2): 279-296, 2007. DOI: 10.1016/j.jbankfin.2006.03.002.

10. Li G. D., Yamaguchi D., Nagai M. A., GM (1, 1)–Markov Chain Combined Model with an Application to Predict the Number of Chinese International Airlines [J], Technological Forecasting and Social Change, 74(8): 1465-1481, 2007. DOI: 10.1016/j.techfore.2006.07.010.

11. Tang J, Wang L, Yao Z., Spatio-Temporal Urban Landscape Change Analysis Using the Markov Chain Model and a Modified Genetic Algorithm [J], International Journal of Remote Sensing, 28(15): 3255-3271, 2007. DOI: 10.1016/j.techfore.2006.07.010.

12. Cruz-Monteagudo M., González-Díaz H., Unified Drug–Target Interaction Thermodynamic Markov Model Using Stochastic Entropies to Predict Multiple Drugs Side Effects [J], European Journal of Medicinal Chemistry, 40(10): 1030-1041, 2005.

13. National Bureau of Statistics of China (NBSC), China Statistical Yearbook, Beijing: China Statistics Press, 2013.